The Bayesian Way — A Probabilistic Philosophy for Market Forecasting

Most people approach markets with a binary mindset: “it will go up or it will go down.”
Bayesian forecasting rejects that false certainty. It treats markets as probabilistic systems where beliefs must update as new data arrives.

1) The Long Term Has a Higher Edge

Noise dominates the day-to-day; fundamentals dominate across quarters and years. Extending the horizon lets base rates (e.g., ~63% of quarters are positive) overpower randomness.

2) You Can Never Be More Than ~70% Right

Elite forecasters still carry ~30% uncertainty. Focus on expected value, sizing, and humility — not perfection.

3) Small Samples Demand Bayesian Smoothing

Thin samples get shrunk toward a broader prior (base rate). A 15/20 (75%) pattern might smooth toward ~68% when anchored to the long-run prior.

4) One Data Point Is Never Enough

Robust forecasts combine multiple views: inflation (CPI, PCE, wages), consumer health, labor markets, and liquidity conditions. Each view updates the posterior; none is decisive alone.

5) Relevant Data Drives Forecasting

Technical indicators summarize price; they don’t drive it. True drivers differ by asset class: inventories and OPEC for oil, inflation expectations and policy for bonds, earnings revisions and PMIs for tech, rate differentials for FX.

6) Everything Is Correlated

Cross-asset linkages matter (rates → risk appetite, oil → inflation → policy → multiples). Bayesian forecasters map these causal webs to avoid isolated conclusions.

Final Thought

Forecasting is Bayesian or it’s wrong. Embrace uncertainty, respect base rates, smooth thin data, and keep updating beliefs as the world changes.